文献类型：期刊文献

英文题名：Neuromuscular Password-Based User Authentication

作者：Jiang, Xinyu[1];Xu, Ke[1];Liu, Xiangyu[2];Dai, Chenyun[1];Clifton, David A.[3];Clancy, Edward A.[4];Akay, Metin[5];Chen, Wei[1]

机构：[1]Fudan Univ, Sch Informat Sci & Technol, Ctr Intelligent Med Elect, Shanghai 200433, Peoples R China;[2]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200237, Peoples R China;[3]Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford OX1 2JD, England;[4]Worcester Polytech Inst, Dept Elect & Comp Engn, Worcester, MA 01609 USA;[5]Univ Houston, Dept Biomed Engn, Houston, TX 77204 USA

年份：2021

卷号：17

期号：4

起止页码：2641

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20210409810843);WOS:【SCI-EXPANDED(收录号:WOS:000607814600033)】；

基金：This work was supported in part by the National Key R&D Program of China under Grant 2017YFE0112000, in part by the Shanghai Pujiang Program under Grant 19PJ1401100, and in part by the Shanghai Municipal Science and Technology Major Project under Grant 2017SHZDZX01. Paper no. TII-20-1930.

语种：英文

外文关键词：Biometrics; high-density surface electromyogram (sEMG); machine learning; neuromuscular password; user authentication

摘要：In this article, we propose a novel neuromuscular password-based user authentication method. The method consists of two parts: surface electromyogram (sEMG) based finger muscle isometric contraction password (FMICP) and neuromuscular biometrics. FMICP can be entered through isometric contraction of different finger muscles in a prescribed order without actual finger movement, which makes it difficult for observers to obtain the password. In our study, the isometric contraction patterns of different finger muscles were recognized through high-density sEMG signals acquired from the right dorsal hand. Moreover, both time-frequency-space domain features at macroscopic level (interference-pattern EMG) and motor neuron firing rate features at microscopic level (via decomposition) were extracted to represent neuromuscular biometrics, serving as a second defense. The FMICP and macro-micro neuromuscular biometrics together form a neuromuscular password. The proposed neuromuscular password achieved an equal error rate (EER) of 0.0128 when impostors entered a wrong FMICP. Even when impostors entered the correct FMICP, the neuromuscular biometrics, as the second defense, inhibited impostors with an EER of 0.1496. To the best of our knowledge, this is the first study to use individually unique neuromuscular information during unobservable muscle isometric contractions for user authentication, with training and testing data acquired on different days.